Sibyl
AI-powered deep research agent. Ask any question — Sibyl searches the web across multiple sources, reads dozens of pages, cross-references findings, and generates an executive-quality research report with analysis, predictions, and citations.
Not just another search summarizer. Sibyl is a research analysis platform — it does structured comparisons, SWOT analysis, Google Trends tracking, event timelines, and financial data visualization. All from a single question.
What Makes Sibyl Different
| Traditional Search | ChatGPT/Perplexity | GPT Researcher | Sibyl | |
|---|---|---|---|---|
| Web search + summary | Yes | Yes | Yes | Yes |
| Multi-source (news, Reddit, Wikipedia) | No | Partial | Partial | Yes (4 engines) |
| Sub-question decomposition | No | No | Yes | Yes |
| Iterative gap-filling (search → analyze → identify gaps → search again) | No | No | Partial | Yes |
| Cross-source analysis (sentiment, consensus, disagreements) | No | No | No | Yes |
| Structured comparison tables | No | No | No | Yes |
| SWOT analysis | No | No | No | Yes |
| Google Trends data | No | No | No | Yes |
| Event timelines | No | No | No | Yes |
| Financial data + charts | No | No | No | Yes |
| MCP server (Claude Code, Cursor) | No | No | No | Yes |
| Multi-LLM (DeepSeek, Gemini, GLM, OpenAI) | No | No | Limited | Yes (auto-detect) |
| PDF reports with embedded charts | No | No | Basic | Yes |
Quick Start
MCP Server (for Claude Code / Cursor)
pip install sibyl-research
claude mcp add sibyl -e DEEPSEEK_API_KEY=sk-... -- sibyl-mcp
Then in Claude Code:
"Research the impact of AI on software engineering jobs over the next 5 years"
"Compare NVIDIA vs AMD vs Intel for AI workloads"
"SWOT analysis of Tesla in 2026"
CLI
pip install sibyl-research
export DEEPSEEK_API_KEY=sk-... # or OPENAI_API_KEY, GEMINI_API_KEY, etc.
# Standard research
sibyl "Canadian housing market outlook 2026"
# Deep research with predictions + market data + PDF
sibyl "Will NVIDIA maintain AI chip dominance?" -d 3 --symbols NVDA,AMD,INTC --pdf
# Chinese output
sibyl "加拿大移民政策变化" -l zh --pdf -o reports/
How It Works
You ask a question
│
├─ Step 1: Decompose into 3-5 focused sub-questions
├─ Step 2: Generate 15-20 diverse search queries
├─ Step 3: Search across 4 engines (DuckDuckGo, Google News, Reddit, Wikipedia)
├─ Step 4: Scrape 15-20 sources (realistic browser headers, retry, Google Cache fallback)
├─ Step 5: Filter sources by relevance (LLM-scored)
├─ Step 6: Analyze each sub-question independently
├─ Step 7: Identify knowledge gaps → auto-search for missing info
├─ Step 8: Cross-reference sources (sentiment, consensus, disagreements)
├─ Step 9: Section-by-section synthesis (Summary, Findings, Analysis, Predictions)
├─ Step 10: Review and refine draft
└─ Output: PDF/Markdown report with Table of Contents, citations, charts
Research Tools (11 MCP tools)
Core Research
| Tool | What it does |
|---|---|
research(query, depth, language) | Full research cycle: search → scrape → analyze → report. Depth 1-3. |
quick_search(query) | Fast web search, returns raw results |
read_url(url) | Extract clean text from any URL |
analyze(text, question) | Analyze provided text with LLM |
Analysis Tools (unique to Sibyl)
| Tool | What it does |
|---|---|
compare(items) | Structured side-by-side comparison table with metrics and recommendation |
swot(subject) | Strengths / Weaknesses / Opportunities / Threats with evidence |
trends(keywords) | Real Google Trends data: interest level, direction, rising searches |
timeline(topic) | Chronological event table with dates and impact assessment |
Financial Data
| Tool | What it does |
|---|---|
fetch_market_data(symbols) | Real stock/ETF prices, trends, moving averages, 52-week range |
chart(symbols) | Generate price trend charts (PNG) |
Output
| Tool | What it does |
|---|---|
save_report(format) | Save as PDF (with embedded charts) and/or Markdown |
Research Depth
| Depth | What happens | LLM calls | Time |
|---|---|---|---|
| 1 (quick) | 2-3 search queries, basic synthesis | ~3 | 20-30s |
| 2 (standard) | Sub-question decomposition, per-question analysis, cross-referencing, review | ~10 | 60-90s |
| 3 (deep) | + Knowledge gap filling, predictions with bull/bear/base case, confidence rating | ~13 | 90-120s |
Multi-Provider Support
Sibyl works with any LLM. Auto-detects from environment variables:
| Provider | Env var | Model |
|---|---|---|
| DeepSeek | DEEPSEEK_API_KEY | deepseek/deepseek-chat |
| OpenAI | OPENAI_API_KEY | gpt-4o-mini |
| Anthropic | ANTHROPIC_API_KEY | claude-sonnet-4-20250514 |
| Gemini | GEMINI_API_KEY | gemini/gemini-2.5-flash |
| GLM (ZhipuAI) | ZHIPUAI_API_KEY | glm-4-flash |
Or configure multiple providers with roles:
# sibyl.yaml
providers:
- model: deepseek/deepseek-chat
api_key: sk-xxx
role: analysis
- model: gemini/gemini-2.5-flash
api_key: xxx
role: fast
- model: openai/glm-4-flash
api_key: xxx
api_base: https://open.bigmodel.cn/api/paas/v4
role: chinese
Example Reports
Reports generated by Sibyl on real topics:
- Federal Reserve interest rate outlook 2026-2027 — 5 pages, 12 findings, 6 sources, analysis of "higher-for-longer" vs "steady easing" debate
- Impact of Trump tariffs on trade 2026 — 5 pages, 10 findings, 4 sources, historical comparison to Smoot-Hawley, second-order effects on AI labor displacement
- AI industry landscape 2026 — Market size ($538B), investment trends ($2.9T infrastructure), regulatory outlook, with NVDA/GOOGL/META stock charts
Requirements
- Python 3.10+
- At least one LLM API key
- No other API keys needed (all search engines are free)
License
MIT